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Systems biology approaches to finding novel pain mediators

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Abstract Chronic pain represents a major health burden; this maladaptive pain state occurs as a consequence of hypersensitivity within the peripheral and central components of the somatosensory system. High throughput technologies (genomics, transciptomics, lipidomics, and proteomics) are now being applied to tissue derived from pain patients as well as experimental pain models to discover novel pain mediators. The use of clustering, meta‐analysis and other techniques can help refine potential candidates. Of particular importance are systems biology methods, such as co‐expression network generating algorithms, which infer potential associations/interactions between molecules and build networks based on these interactions. Protein–protein interaction networks allow the lists of potential targets generated by these different platforms to be analyzed in their biological context. Outputs from these different methods must also be related to the clinical pain phenotype. The improved and standardized phenotyping of pain symptoms and sensory signs enables much better subject stratification. Our hope is that, in the future, the use of computational approaches to integrate datasets including sensory phenotype as well as the outputs of high throughput technologies will help define novel pain mediators and provide insights into the pathogenesis of chronic pain. WIREs Syst Biol Med 2013, 5:11–35. doi: 10.1002/wsbm.1192 This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Biological Mechanisms > Regulatory Biology Laboratory Methods and Technologies > RNA Methods

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Transmission of nociceptive information and mechanisms of peripheral and central sensitization. (a) In physiological conditions, noxious stimuli are detected by the peripheral terminals of DRG neurons and transmitted to the spinal cord by DRG central terminals. These synapse onto cells in the dorsal horn of the spinal cord which will then convey the information to the brain. (b) In inflammatory pain, pain mediators released by injured tissue or recruited immune cells, contribute to sensitization of peripheral nerve terminals as well as enhanced transmission of nociceptive information within the dorsal horn (central sensitization) leading to pain hypersensitivity. (c) Neuropathic pain occurs following injury to the somatosensory nervous system. This results in altered signaling between neurons, glia and immune cells leading to ectopic activity in primary afferents as well as altered synaptic transmission within the central nervous system. (I) Tissue injury (e.g. compromised skin barrier), releases pain mediators which trigger immune cell recruitment. Such immune cells (macrophages, mast cells, and neutrophils) release further mediators. All these mediators interact with receptors expressed on peripheral nerve terminals (see Table 1). GPCR: G‐Protein coupled receptors; ASIC: acid‐sensing ion channels; P2X: ionotopic purine receptors; TRP: transient receptor potential receptors; RTK: receptor tyrosine kinases (e.g. growth factor receptors). (II) Central sensitization occurs as a consequence of increased release of neuromodulators by the central terminals of primary afferent terminals enhancing excitatory glutamatergic transmission. (III) Within an injured nerve, Schwann cell de‐differentiation and macrophage infiltration is accompanied by inflammatory mediator release. Both injured and neighboring uninjured fibers develop ectopic activity (not shown, see Ref 29). (IV) Within the DRG (following peripheral nerve injury) satellite cells proliferate, sympathetic fibers sprout around injured neurons (not shown) and transcriptional changes occur in neurons. These events lead to enhanced pain transmission. (V) Within the dorsal horn, microglia, and astrocytes proliferate and adopt a pro‐inflammatory phenotype and both signaling events and transcriptional changes in dorsal horn neurons contribute to central sensitization. For extensive reviews see Refs 7,19.

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Utility of examining pain genes as part of a system in the PainNetworks website (produced using http://PainNetworks.org). Here, a known pain gene, nitric oxide synthase 1 (neuronal ‐ NOS1) is displayed with the interactors of its corresponding protein in human. NOS1 has been established as a pain gene in mouse, however due to the sparsity of murine PPI data, and because we are interested in interactions also found in humans, we look at its human orthologue. This allows us to overlay experimental data which can be complementary to the interaction data. For example we can see that SYN1 (encoding synapsin 1) and NOS1 are co‐expressed in a pain microarray experiment from mouse and interact physically in human. By combining the two orthologous data‐types we can improve confidence in our networks. Legend: Border color corresponds to fold change in a pain microarray dataset from Rat (red: upregulated in a pain state, blue: downregulated in a pain state), thick lines demonstrate interactions that are also co‐expressed in a spinal nerve ligation dataset. Network edges represent: physical interactions from an experiment such as yeast‐2 hybrid (blue line), physical interaction from an experiment in mouse or rat (green line), and other, not necessarily direct interactions, for example, co‐membership of a complex (dotted line). For example, CaMK2A is detected by this approach and mice carrying a point mutation in this gene display reduced ongoing pain after formalin injection.148

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Possible steps in the analysis of high‐throughput data. The top level shows the expected output from the technologies depicted in Figure 2. Below these are examples of what can be done with the data, explained further in the section “Data Analysis of Systems Level Studies”. The expression matrix has three main routes: a useful first pass is to look at the samples to see what is causing the largest amount of variance in the data. It can also be turned into a dissimilarity matrix, which serves as input into the clustering and co‐expression network generating techniques. Finally, differential expression analysis can be performed, to produce a list of differentially expressed genes (following multiple testing correction to reduce false positives). These can be studied manually (often with the help of a gene set enrichment software, not shown), or can be combined with other data sources, such as the protein–protein interaction network shown, to aid data interpretation. The SNP/allele frequencies and exomic sequencing data is used to find SNPs that occur more frequently in the population of interest (e.g. among people with high pain hypersensitivity) than the control group. Note that when using SNP‐arrays for GWAS, it does not follow that the SNP detected is responsible for the pain phenotype, but that it is likely to be close to the detected SNP in the genome, and in linkage disequilibrium with it. Therefore further study of the area near this SNP is likely to be needed. In addition, gene prioritization tools, for example PPIs, can be used to prioritize the genes near the associated SNP as most likely to be disease causative, which is why we connect the results to the PPI network. Exomic/genomic sequencing directly sequences the exons/genome and allows a much finer map of the areas of a genome associated with a disease. However this does not get rid of the linkage disequilibrium problem. Currently familial studies present a good way to reduce this problem. Note that the co‐expression networks and PPI networks are not mutually exclusive: co‐expressed genes could be added to the PPI network as extra edges.

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High‐throughput technologies to generate systems level data. Studying pain at the systems level: The top level shows different types of tissue used: dorsal root ganglia (DRG), nerve and Spinal Cord, as well as brain and skin are typically used for expression studies and blood for DNA extraction, and can be obtained from humans, rats or mice. The middle level contains two panels, one showing expression‐based techniques, and the other genetics‐based techniques. The 2D proteomics gel picture was contributed by Kersti Karu and Kenji Okuse and the GWAS picture was generated using R code by Stephen Turner (http://dx.doi.org/10.1038/npre.2011.6070.1). The bottom level shows the typical output of these analyses. For expression‐based studies the typical output is a matrix, with the genes/proteins/lipids of interest, and the resultant expression values. For genetic analysis, typical outputs are lists of SNPs or DNA sequences.

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